# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run inference on images, videos, directories, streams, etc.

Usage:
    $ python path/to/detect.py --weights yolov5s.pt --source 0  # webcam
                                                             img.jpg  # image
                                                             vid.mp4  # video
                                                             path/  # directory
                                                             path/*.jpg  # glob
                                                             'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                                                             'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
"""

import argparse
import os
import sys
from pathlib import Path

import subprocess as sp

import cv2
import torch
import torch.backends.cudnn as cudnn

FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

from models.common import DetectMultiBackend
from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
                           increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, time_sync


@torch.no_grad()
def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
        source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for webcam
        imgsz=640,  # inference size (pixels)
        conf_thres=0.25,  # confidence threshold
        iou_thres=0.45,  # NMS IOU threshold
        max_det=1000,  # maximum detections per image
        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        view_img=False,  # show results
        save_txt=False,  # save results to *.txt
        save_conf=False,  # save confidences in --save-txt labels
        save_crop=False,  # save cropped prediction boxes
        nosave=False,  # do not save images/videos
        classes=None,  # filter by class: --class 0, or --class 0 2 3
        agnostic_nms=False,  # class-agnostic NMS
        augment=False,  # augmented inference
        visualize=False,  # visualize features
        update=False,  # update all models
        project=ROOT / 'runs/detect',  # save results to project/name
        name='exp',  # save results to project/name
        exist_ok=False,  # existing project/name ok, do not increment
        line_thickness=3,  # bounding box thickness (pixels)
        hide_labels=False,  # hide labels
        hide_conf=False,  # hide confidences
        half=False,  # use FP16 half-precision inference
        dnn=False,  # use OpenCV DNN for ONNX inference
        push_stream=False,
        outstream='rtmp://188.116.30.54:1935/live/test_ai',
        output_fps=0,
        save_frame_fps = 5,
        outpath = None # output path for images and videos, support directory or whole path
        ):
    source = str(source)
    save_img = not nosave and not source.endswith('.txt')  # save inference images
    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
    webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
    if is_url and is_file:
        source = check_file(source)  # download

    # Directories  ################### Add by video live: outpath
    if not outpath: 
        save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
    else:
        # judge whether is a directory or image/video file
        str0, ext0 = os.path.splitext(outpath)
        if ext0 == '': # directory
            save_dir = outpath
        else: # file
            dir0, name0 = os.path.split(outpath)
            save_dir = dir0
        save_dir = Path(save_dir)
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir


    # Load model
    device = select_device(device)
    model = DetectMultiBackend(weights, device=device, dnn=dnn)
    stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine
    imgsz = check_img_size(imgsz, s=stride)  # check image size

    # Half
    half &= (pt or engine) and device.type != 'cpu'  # half precision only supported by PyTorch on CUDA
    if pt:
        model.model.half() if half else model.model.float()

    # Dataloader
    if webcam:
        view_img = check_imshow()
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)
        bs = len(dataset)  # batch_size

        ################### Add by video live
        cap = cv2.VideoCapture(source)
        fps = cap.get(cv2.CAP_PROP_FPS)
        width =cap.get(cv2.CAP_PROP_FRAME_WIDTH)
        height =cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
        outfps = fps
        if output_fps != 0:
            outfps = output_fps
        command = ['ffmpeg',
        '-y',
        '-f', 'rawvideo',
        '-vcodec','rawvideo',
        '-pix_fmt', 'bgr24',
        '-s', "{}x{}".format(int(width), int(height)),
        '-r', str(outfps),
        '-i', '-',
        '-c:v', 'libx264',
        '-pix_fmt', 'yuv420p',
        '-preset', 'ultrafast',
        '-f', 'flv', 
        outstream]
        pipe = sp.Popen(command, stdin=sp.PIPE)



    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)
        bs = 1  # batch_size
    vid_path, vid_writer = [None] * bs, [None] * bs

    # Run inference
    if pt and device.type != 'cpu':
        model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters())))  # warmup
    dt, seen = [0.0, 0.0, 0.0], 0
    
    ################### Add by video live
    frstframe = True  # just used once to copy first frame
    ind_labels = save_txt
    ncount = 0
    for path, im, im0s, vid_cap, s in dataset:

        t1 = time_sync()

        ################### Add by video live
        """ this again helps in reducing delay i checked for hours earlier was causing delay after hours but this changed 
        everything and no delay was found
        """
        """
        though these are few lines but they changed everything becuase if check closely this loop keeps on detecting even 
        if no new frame was update and causes latency for new frames but these line check that 
        if new frame and older one are same or not
        """

        if webcam:  ################### Add by video live
            if frstframe:
                frstframe=False
                previous = im #.clone()

            if torch.all(torch.eq(torch.from_numpy(previous), torch.from_numpy(im))):
                continue

            previous = im #.clone()
        
        
        im = torch.from_numpy(im).to(device)
        im = im.half() if half else im.float()  # uint8 to fp16/32
        im /= 255  # 0 - 255 to 0.0 - 1.0
        if len(im.shape) == 3:
            im = im[None]  # expand for batch dim
        
        t2 = time_sync()
        dt[0] += t2 - t1


        # Inference
        visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
        pred = model(im, augment=augment, visualize=visualize)
        t3 = time_sync()
        dt[1] += t3 - t2

        # NMS
        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
        dt[2] += time_sync() - t3

        # Second-stage classifier (optional)
        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)


        ################### Add by video live: save frame images and labels each 20 frames
        # nnn = dataset.count if webcam else getattr(dataset, 'frame', 0)
        # print(nnn) # % (int(24/save_frame_fps))
        # if webcam:
        #     pre_name = 'video_'
        # else:
        #     pre = Path(path)
        #     pre_name = pre.stem
        # frame_path = str(save_dir / 'labels' / pre_name) + ('' if dataset.mode == 'image' else f'_{nnn}')
        # print(ncount, type(save_frame_fps), save_frame_fps)
        if ind_labels:
            if ncount % (int(24*save_frame_fps)) != 0:
                save_txt = False
            else:
                save_txt = True
        
    
        # Process predictions
        for i, det in enumerate(pred):  # per image
            seen += 1
            if webcam:  # batch_size >= 1
                p, im0, frame = path[i], im0s[i].copy(), dataset.count
                s += f'{i}: '
            else:
                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)

            # if save_txt:
                ################### Add by video live: save orignal images
                # cv2.imwrite(frame_path + '.jpg', im0)

            p = Path(p)  # to Path

            ################### Add by video live: outpath
            if os.path.splitext(outpath)[1] != '': # file
                if dataset.mode == 'image':
                    save_path = str(save_dir / os.path.basename(outpath))
                else: # video output .mp4
                    stem, ext = os.path.splitext(os.path.basename(outpath))
                    out_name = stem + '.mp4'
                    save_path = str(save_dir / out_name)
            else:
                save_path = str(save_dir / p.name)  # im.jpg

            txt_path = str(save_dir / 'labels' / p.stem) + ('_input' if dataset.mode == 'image' else f'_{frame}')  # im.txt
            s += '%gx%g ' % im.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            imc = im0.copy() if save_crop else im0  # for save_crop
            annotator = Annotator(im0, line_width=line_thickness, example=str(names))
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, -1].unique():
                    n = (det[:, -1] == c).sum()  # detections per class
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                # Write results
                for *xyxy, conf, cls in reversed(det):
                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                        with open(txt_path + '.txt', 'a') as f:
                            f.write(('%g ' * len(line)).rstrip() % line + '\n')
                            # 
                            ################### Add by video live: save orignal images
                            cv2.imwrite(txt_path + '.jpg', imc)

                    ################### Add by video live for: push_stream
                    if save_img or save_crop or view_img or push_stream:  # Add bbox to image
                        c = int(cls)  # integer class
                        label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                        annotator.box_label(xyxy, label, color=colors(c, True))
                        if save_crop:
                            save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)

            # Print time (inference-only)
            LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
            
            ################### Add by video live
            # print(ncount, frame, save_txt)
            if ncount > 24*save_frame_fps*50:  # save each frame per save_frame_fps seconds, totally save 50 frames.
                ncount = 24*save_frame_fps*50 + 3
            else:
                ncount = ncount + 1

            # Stream results
            im0 = annotator.result()

            ################### Add by video live
            if webcam:
                pipe.stdin.write(im0.tobytes()) # tostring()   
            if save_txt:
                cv2.imwrite(txt_path + '_bbox.jpg', im0)  # save frame images with bbox


            if view_img:
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)  # 1 millisecond

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                else:  # 'video' or 'stream'
                    if vid_path[i] != save_path:  # new video
                        vid_path[i] = save_path
                        if isinstance(vid_writer[i], cv2.VideoWriter):
                            vid_writer[i].release()  # release previous video writer
                        if vid_cap:  # video
                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        else:  # stream
                            fps, w, h = 30, im0.shape[1], im0.shape[0]
                            save_path += '.mp4'
                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                    vid_writer[i].write(im0)

    # Print results
    t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
    if save_txt or save_img:
        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
    if update:
        strip_optimizer(weights)  # update model (to fix SourceChangeWarning)


def parse_opt():
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)')
    parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')
    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='show results')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
    parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
    parser.add_argument('--nosave', action='store_true', help='do not save images/videos with bbox')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--visualize', action='store_true', help='visualize features')
    parser.add_argument('--update', action='store_true', help='update all models')
    parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
    parser.add_argument('--name', default='exp', help='save results to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
    

    ################### Add by video live
    parser.add_argument('--save-frame-fps', type=float, default=5, help='save frame number per n seconds')
    parser.add_argument('--output-fps', type=int, default=24, help='fps of output stream')
    parser.add_argument('--push-stream', action='store_true', help='push results to server')
    parser.add_argument('--outstream', type=str, default='rtmp://192.168.3.99/live/test_ai', help='Output stream')
    parser.add_argument('--outpath', type=str, default=None, help='output path and filename of resulted image/video')

    opt = parser.parse_args()
    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
    print_args(FILE.stem, opt)
    return opt


def main(opt):
    check_requirements(exclude=('tensorboard', 'thop'))
    run(**vars(opt))


if __name__ == "__main__":
    opt = parse_opt()
    main(opt)
